Pradra Studio — ML and LLM in one place
From business problem to production AI.
Machine learning and large language models (LLM) in one place. One place to take an AI solution through its whole life — the data, the training, the deployment, and everything that happens after it goes live.
ML dan LLM dalam satu tempat — bangun, terapkan, dan pantau solusi AI di satu platform.
Solutions
New solutionPredictions today
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p95 latency
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Stop asking which algorithm. Start asking which business problem.
Most AI work stalls in experiments: models get built, prove something in a notebook, and never make it into the product. Pradra Studio is organized around the outcome — each solution owns its data, its models, and its place in production, so the work you do once keeps paying off.
“Which algorithm should I use?”
“What business problem am I solving?”
- A solution outlives any single model behind it.
- The whole team sees the same picture — not scattered notebooks.
- Going live is part of the process, not a separate project.
How it works
01
Define the solution
Start from the business problem — reducing churn, predicting failures, forecasting demand — and bring in the data that describes it.
02
Train and evaluate
Train models on your data and let the platform keep track of every run. Nothing reaches production without passing the quality bar you set.
03
Deploy and monitor
Put the model live behind a stable endpoint, watch its health and accuracy, and retrain when the real world drifts away from the data.
Everything after the model matters just as much.
Data you can trust
Upload and validate datasets once, then reuse them across every solution in your workspace.
Experiments on record
Every training run is captured — what went in, what came out, and how well it did.
Quality gates
A model must pass the acceptance criteria you define before it can ever reach production.
One-click deployment
Promote an approved model to a live endpoint your applications call directly.
Always monitored
Latency, errors, and prediction quality are tracked in production — with alerts when something slips.
Improves over time
When data drifts, the platform retrains, re-checks quality, and asks for your approval before anything changes.
Build once. Keep improving.
Create a solution, take it to production, and let it get better with every retraining — all in one place.